Linear Regression
Linear regression is one of the most widely used techniques in biostatistics. It is ideal when the relationship between one or more independent variables (predictors) and a dependent variable is assumed to be linear. BioStat Prime supports a variety of linear modeling methods, making it suitable for both beginners and advanced analysts working with health, clinical, and food science data. Linear regression assumes a straight-line (or planar, in multivariable contexts) relationship between the dependent variable and predictor(s). Ideal when responses change proportionally with variables.
Comprehensive Linear Regression Analysis Support in BioStat Prime
BioStat Prime offers a robust suite of regression techniques to handle a wide range of statistical modeling needs — from simple linear regression to advanced survival models. The following regression methods are supported:
Cox, Advanced
Cox, Basic
Cox, Binary Time-Dependent Covariates
Cox, Fine-Gray
Cox Regression, Multiple Models
Cox, Stratified
Linear, Advanced
Linear, Basic
Logistic, Advanced
Linear Regression, Multiple Models
Logistic, Basic
Multinomial Logit
Logistic, Conditional
Logistic Regression, Multiple Models
Ordinal
Quantile
Parametric Survival Regression